industry-scanner

Industry Scanner

Daily deep-research agent that scans the internet for everything relevant to a client's industry, then generates strategic GTM opportunities based on what it finds.

Quick Start

Run an industry scan for <client>. Use the config at clients/<client>/config/industry-scanner.json.

Or for a weekly deeper scan:

Run a weekly industry scan for <client> with --lookback 7.

Inputs

• Client name — determines which config and context files to load
• Lookback period (optional) — 1 for daily (default), 7 for weekly deep scan
• Focus area (optional) — limit scan to specific categories (e.g., "competitors only", "events only")

Step-by-Step Process

Phase 1: Load Configuration

1. Read clients/<client>/config/industry-scanner.json — this contains all the keywords, sources, competitors, and URLs to scan
2. Read clients/<client>/context.md — need the ICP, value props, and positioning to generate relevant strategies
3. Set the lookback period: use 1 day for daily scans, 7 for weekly, or whatever the user specifies
4. Note today's date for the output filename

If no client config exists, ask the user for the key inputs and offer to create one from the example at skills/industry-scanner/config/example-config.json.

Phase 2: Data Collection

Run these data sources in parallel where possible. Skip any source that isn't configured. For each source, use the existing skill's CLI or tool as documented.

IMPORTANT: Run as many of these bash commands in parallel as possible to minimize total scan time. Sources are independent of each other.

2A. Web Search (built-in WebSearch tool)

Run 5-8 web searches combining the configured web_search_queries with time-sensitive modifiers. Examples:

• "<industry keyword> news this week"
• "<competitor name> shutdown OR closing OR acquired 2026"
• "<industry> conference 2026 speaker applications"
• "<industry keyword> new regulation OR policy change"
• "<competitor name> layoffs OR pivot OR rebrand"

Also search for each competitor name directly to catch any recent news.

2B. Industry Blogs & Publications

python3 skills/blog-feed-monitor/scripts/scrape_blogs.py \
  --urls "<comma-separated blog_urls from config>" \
  --days <lookback> --output json

Read skills/blog-feed-monitor/SKILL.md for full CLI reference.

2C. Reddit

For each configured subreddit, run:

python3 skills/reddit-post-finder/scripts/search_reddit.py \
  --subreddit "<comma-separated subreddits from config>" \
  --keywords "<comma-separated reddit_keywords from config>" \
  --days <lookback> --sort hot --output json

Also run a separate search with --sort top --time week to catch high-engagement posts.

Read skills/reddit-post-finder/SKILL.md for full CLI reference.

2D. Twitter/X

For each configured Twitter query:

python3 skills/twitter-mention-tracker/scripts/search_twitter.py \
  --query "<twitter_query>" \
  --since <yesterday-YYYY-MM-DD> --until <today-YYYY-MM-DD> \
  --max-tweets 30 --output json

Read skills/twitter-mention-tracker/SKILL.md for full CLI reference.

2E. LinkedIn

Search each configured LinkedIn keyword via the linkedin-post-research skill.

Delegate to the linkedin-post-research skill (uses the apimaestro~linkedin-posts-search-scraper-no-cookies Apify actor). Search each keyword with date_posted: "past-day" (or "past-week" for weekly scans).

Read skills/linkedin-post-research/SKILL.md for the full Apify workflow.

2F. Hacker News

python3 skills/hacker-news-scraper/scripts/search_hn.py \
  --query "<hn_query>" --days <lookback> --output json

Run once per configured hn_queries entry. Read skills/hacker-news-scraper/SKILL.md for full CLI reference.

2G. RSS News Feeds

If the client has an accounting-news-monitor (or similar) configured:

python3 skills/accounting-news-monitor/scripts/monitor_news.py \
  --new-only --days <lookback> --output json

Read skills/accounting-news-monitor/SKILL.md for full CLI reference.

2H. Newsletter Inbox

If the client has newsletter monitoring configured:

python3 skills/newsletter-monitor/scripts/scan_newsletters.py \
  --days <lookback> --output json

Read skills/newsletter-monitor/SKILL.md for full CLI reference.

2I. Review Sites

For each configured review URL:

python3 skills/review-site-scraper/scripts/scrape_reviews.py \
  --platform <platform> --url "<review_url>" \
  --days <lookback> --max-reviews 20 --output json

Read skills/review-site-scraper/SKILL.md for full CLI reference.

Phase 3: Consolidate & Categorize

After all data collection completes, consolidate the results:

1. Deduplicate — items appearing across multiple sources (e.g., a news story on both a blog and Reddit). Keep the richest version but note multi-source appearance (higher signal).

1. Categorize each item into one of these types:

| Category | What to Look For | |----------|-----------------| | Competitor News | Shutdowns, launches, funding, pivots, negative reviews, leadership changes, pricing changes | | Industry Events | Upcoming conferences, webinars, meetups, speaker slots, CFPs, award nominations | | Market Trends | Viral discussions, hot topics, emerging themes, sentiment shifts, adoption data | | Regulatory / Policy | New regulations, compliance changes, government actions, standards updates | | People Moves | Key hires, departures, promotions at competitors or target companies | | Technology | New product launches, integrations, platform changes, deprecations | | Funding / M&A | Acquisitions, mergers, funding rounds, PE investments, IPO signals | | Pain Points | People publicly complaining about problems the client solves | | Content Opportunities | Trending content, viral posts, gaps in existing coverage, unanswered questions |

1. Rate relevance — High / Medium / Low based on how directly it relates to the client's ICP and value props.

1. Filter out noise — Drop items rated Low relevance unless they're genuinely noteworthy. The goal is signal, not volume.

Phase 4: Generate Strategic Opportunities

Review the consolidated intelligence and identify items (or clusters of related items) that present genuine GTM opportunities.

CRITICAL: Do NOT force-fit a strategy for every item. Many items are just "good to know" — that's fine, they go in the intelligence briefing. Only generate strategy ideas where there is a real, actionable opportunity that could meaningfully impact growth.

For each genuine opportunity, produce:

| Field | Description | |-------|-------------| | Trigger | What happened — the intelligence item(s) that sparked this idea | | Strategy | What to do about it — specific and actionable, not vague | | Tactics | 2-4 concrete next steps with skill references where applicable | | Urgency | Immediate (do this today/this week), Soon (next 2 weeks), or Evergreen | | Effort | Low (1-2 hours), Medium (half day), High (multi-day project) | | Expected Impact | Why this could matter — who it reaches, what it could generate |

Strategy Patterns to Draw From

Use these as inspiration, not as a checklist. Match the pattern to the trigger:

Competitor in trouble (shutdown, bad reviews, layoffs, pivot):

• Publish a migration/comparison guide targeting their customers
• Find their customers via review sites, LinkedIn posts mentioning them → outreach
• Engage on social posts where people discuss the shutdown/issues
• Create "alternative to X" content for SEO capture
• Skills: web-archive-scraper (recover their customer list), review-site-scraper (find reviewers), linkedin-post-research (find posts about them), cold-email-outreach

Industry event coming up:

• Apply to speak (if speaker slots are open)
• Plan pre-event outreach to attendees (skill: `luma